[Take-home messages from the 2nd COPD 2023 Biennial of the French Society of Respiratory Diseases. Placing the patient at the center of the care pathway]. / Principaux messages de la deuxième Biennale BPCO 2023 de la SPLF. Le patient au cœur du parcours de soins.

INTRODUCTION: The second COPD Biennial organized by the COPD working group of the French Society of Respiratory Diseases took place in Paris (Cochin) on 13th December 2023. STATE OF THE ART: Major trends in 2023 were discussed; they encompassed concepts, definitions, biologics, care pathways, pulmonary rehabilitation and complex situations entailed by respiratory infections, cardiovascular comorbidities and pulmonary hypertension, and modalities of oxygen therapy and ventilation. PERSPECTIVES: The different talks underlined major changes in COPD including the concepts of pre-COPD, etiotypes, health trajectories and new definitions of exacerbation. Recent results in biologics for COPD open the door to new pharmacological options. Assessment of current care pathways in France highlighted some causes for concern. For example, pulmonary rehabilitation is a key but insufficiently practiced element. Respiratory infections require careful assessment and treatments. Diagnosis and treatment of cardiovascular comorbidities and pulmonary hypertension are of paramount importance. As of late, oxygen therapy and ventilation modalities have evolved, and are beginning to afford more personalized options. CONCLUSIONS: As regards COPD, a personalized approach is crucial, placing the patient at the center of the care pathway and facilitating coordination between healthcare providers.

A metabolomics study: Could plasma metabolites be a guide for the prevention of tamsulosin side effects?

BACKGROUND: The understanding of precision medicine, which aims for high efficacy and low toxicity in treatments, has gained more importance with omics technologies. In this study, it was aimed to reach new suggestions for low-toxicity treatment by clarifying the relationship between tamsulosin side effects and metabolome profiles. MATERIALS AND METHODS: Plasma samples of control and tamsulosin-treated rats were analyzed by LC-Q-TOF/MS/MS. MS/MS data was processed in XCMS software for the identification of metabolite and metabolic pathway analysis. Data were classified with MATLAB 2019b for multivariate data analysis. 34m/z values were found to be significantly different between the drug and control groups (P≤0.01 and fold analysis≥1.5) and identified by comparing METLIN and HMDB databases. RESULTS: According to multivariate data analysis, α-Linolenic Acid, Thiamine, Retinoic acid, 1.25-Dihydroxyvitamin D3-26.23-Lactone, L-Glutamine, L-Serine, Retinaldehyde, Sphingosine 1-phosphate, L-Lysine, 23S.25-Dihydroxyvitamin D3, Sphinganine, L-Cysteine, Uridine 5'-diphosphate, Calcidiol, L-Tryptophan, L-Alanine levels changed significantly compared to the control group. Differences in the metabolisms of Retinol, Sphingolipid, Alanine-Aspartate-Glutamate, Glutathione, Fatty Acid, Tryptophan, and biosynthesis of Aminoacyl-tRNA, and Unsaturated Fatty Acid have been successfully demonstrated by metabolic pathway analysis. According to our study, vitamin A and D supplements can be recommended to prevent side effects such as asthenia, rhinitis, nasal congestion, dizziness and IFIS in the treatment of tamsulosin. Alteration of aminoacyl-tRNA biosynthesis and sphingolipid metabolism pathways during tamsulosin treatment is effective in the occurrence of nasal congestion. CONCLUSIONS: Our study provides important information for tamsulosin therapy with high efficacy and low side effects in precision medicine.

Web App for prediction of hospitalisation in Intensive Care Unit by covid-19 / Web App para la predicción de hospitalización em la Unidad de Cuidados Intensivos por covid-19 / Web App para a predição de internação em Unidade de Terapia Intensiva por covid-19

ABSTRACT Objective: To develop a Web App from a predictive model to estimate the risk of Intensive Care Unit (ICU) admission for patients with covid-19. Methods: An applied technological production research was carried out with the development of Streamlit using Python, considering the decision tree model that presented the best performance (AUC 0.668). Results: Based on the variables associated with Precision Nursing, Streamlit stratifies patients admitted to clinical units who are most likely to be admitted to the Intensive Care Unit, serving as a decision-making support tool for healthcare professionals. Final considerations: The performance of the model may have been influenced by the start of vaccination during the data collection period, however, the Web App via Streamlit proved to be a feasible tool for presenting research results, due to the ease of understanding by nurses and its potential for supporting clinical decision-making.

RESUMEN Objetivo: Desarrollar una Web App a partir de un modelo predictivo para estimar el riesgo de ingreso a la Unidad de Cuidados Intensivos (UCI) para pacientes con covid-19. Métodos: Se realizó una investigación de producción tecnológica aplicada con el desarrollo de Streamlit utilizando Python, considerando el modelo de árbol de decisiones que presentó el mejor rendimiento (AUC 0.668). Resultados: Basado en las variables asociadas con la Enfermería de Precisión, Streamlit estratifica a los pacientes ingresados en unidades clínicas que tienen más probabilidades de ser admitidos en la Unidad de Cuidados Intensivos, sirviendo como una herramienta de apoyo para la toma de decisiones para los profesionales de la salud. Consideraciones finales: El rendimiento del modelo puede haber sido influenciado por el inicio de la vacunación durante el período de recolección de datos. La Web App a través de Streamlit demostró ser una herramienta factible para presentar los resultados, debido a la facilidad de comprensión y su potencial para apoyar la toma de decisiones clínicas.

RESUMO Objetivo: Desenvolver um Web App a partir de um modelo preditivo para estimar o risco de internação de pacientes com covid-19 em UTI. Métodos: Realizou-se uma pesquisa aplicada de produção tecnológica com o desenvolvimento do Streamlit a partir do Python, considerando o modelo de árvore de decisão que apresentou o melhor desempenho (AUC 0.668). Resultados: A partir das variáveis associadas à Enfermagem de Precisão, o Streamlit estratifica os pacientes internados nas unidades clínicas com maior probabilidade de internação em Unidade de Terapia Intensiva, funcionando como uma ferramenta de apoio à tomada de decisão dos profissionais de saúde. Considerações finais: A performance do modelo pode ter sido influenciada pelo início da vacinação no período de coleta de dados, no entanto, o Web App via Streamlit mostrou-se uma ferramenta viável para a apresentação dos resultados de pesquisa, devido à facilidade de entendimento por parte dos enfermeiros e pelo potencial de apoio à decisão clínica.

[The promise of omics in the precision medicine era]. / Les promesses des sciences omiques à l'ère de la médecine de précision.

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

[Take-home messages from the COPD 2021 biennial of the French Society of Respiratory Diseases. Understanding to so as to better innovate]. / Principaux messages de la première Biennale BPCO 2021 de la SPLF. Mieux comprendre pour innover.

INTRODUCTION: The first COPD biennial organized by the French Society of Respiratory Diseases (SPLF) took place on 17 December 2021. STATE OF THE ART: The objective of the biennial was to discuss current knowledge regarding COPD pathophysiology, current treatments, research development, and future therapeutic approaches. PERSPECTIVES: The different lecturers laid emphasis on the complexity of pathophysiologic mechanisms including bronchial, bronchiolar and parenchymal alterations, and also dwelt on the role of microbiota composition in COPD pathenogenesis. They pointed out that addition to inhaled treatments, ventilatory support and endoscopic approaches have been increasingly optimized. The development of new therapeutic pathways such as biotherapy and cell therapy (stem cells…) call for further exploration. CONCLUSIONS: The dynamism of COPD research was repeatedly underlined, and needs to be further reinforced, the objective being to "understand so as to better innovate" so as to develop effective new strategies for treatment and management of COPD.

[Applications of artificial intelligence to new drug development]. / Applications de l'intelligence artificielle au développement de nouveaux médicaments.

Artificial intelligence (AI) encompasses technologies recapitulating four dimensions of human intelligence, i.e. sensing, thinking, acting and learning. The convergence of technological advances in those fields allows to integrate massive data and build probabilistic models of a problem. The latter can be continuously updated by incorporating new data to inform decision-making and predict the future. In support of drug discovery and development, AI allows to generate disease models using data obtained following extensive molecular profiling of patients. Given its superior computational power, AI can integrate those big multimodal data to generate models allowing: (i) to represent patient heterogeneity; and (ii) identify therapeutic targets with inferences of causality in the pathophysiology. Additional computational analyses can help identifying and optimizing drugs interacting with these targets, or even repurposing existing molecules for a new indication. AI-based modeling further supports the identification of biomarkers of efficacy, the selection of appropriate combination therapies and the design of innovative clinical studies with virtual placebo groups. The convergence of biotechnologies, drug sciences and AI is fostering the emergence of a computational precision medicine predicted to yield therapies or preventive measures precisely tailored to patient characteristics in terms of their physiology, disease features and environmental risk exposure.

[Pharmaceutical biotechnologies: drawing out the genome to develop, improve and personalize therapies and patient care]. / L'évolution des biotechnologies pharmaceutiques : faire parler le génome pour développer, améliorer et personnaliser les thérapies et la prise en charge des patients.

Thanks to high-throughput sequencing with technologies, which allow the reading of more than 100 million bases in one day with reduced cost, our knowledge about prokaryotic, eukaryotic and viral genomes has significantly increased since the 2000s. The multiplication of genetic engineering tools including DNA polymerases, restriction enzymes and genome editing enzymes and the development of other "omic" technologies such as transcriptomics, proteomics, and metabolomics, have allowed pharmaceutical biotechnologies to favor the identification and validation of various diagnostic and therapeutic targets underlying the development of targeted therapies. Indeed, these advances have supported a better understanding of pathologies as well as an improvement in their diagnosis, through the identification of biomarkers, considerably favoring more personalized and more efficient patients' care. The orientation towards more targeted treatments was initiated by the development of recombinant proteins, and more recently monoclonal antibodies, which can block protein functions and/or modulate immune responses. Gene therapy is also promising, by inserting a gene into patients' cells to overcome rare diseases and autologous or heterologous cell-based therapies are also under development. Although their history is recent, pharmaceutical biotechnologies are evolving rapidly and offer exciting prospects at the dawn of precision medicine.

TITLE: L'évolution des biotechnologies pharmaceutiques : faire parler le génome pour développer, améliorer et personnaliser les thérapies et la prise en charge des patients. ABSTRACT: Grâce à l'accroissement des connaissances associées aux génomes permis par le séquençage à haut débit, à la multiplication des outils d'ingénierie génétique ainsi qu'aux autres technologies « omiques ¼, les biotechnologies pharmaceutiques ont favorisé l'identification et la validation de nombreuses cibles diagnostiques et thérapeutiques et ont ainsi conduit au développement de nombreuses thérapies ciblées. Bien que leur histoire soit récente, les biotechnologies pharmaceutiques évoluent rapidement et offrent des perspectives réjouissantes à l'aube de la médecine de précision.

The impact of epigenetics on cardiovascular disease.

Mortality and morbidity from cardiovascular diseases (CVDs) represents a huge burden to society. It is recognized that environmental factors and individual lifestyles play important roles in disease susceptibility, but the link between these external risk factors and our genetics has been unclear. However, the discovery of sequence-independent heritable DNA changes (epigenetics) have helped us to explain the link between genes and the environment. Multiple diverse epigenetic processes, including DNA methylation, histone modification, and the expression of non-coding RNA molecules affect the expression of genes that produce important changes in cellular differentiation and function, influencing the health and adaptability of the organism. CVDs such as congenital heart disease, cardiomyopathy, heart failure, cardiac fibrosis, hypertension, and atherosclerosis are now being viewed as much more complex and dynamic disorders. The role of epigenetics in these and other CVDs is currently under intense scrutiny, and we can expect important insights to emerge, including novel biomarkers and new approaches to enable precision medicine. This review summarizes the recent advances in our understanding of the role of epigenetics in CVD.

[Advanced practice nurse in oncology]. / Infirmière en pratique avancée en cancérologie.

In many countries, health systems are facing major difficulties in maintaining the quality, continuity and safety of the care provision. That is why healthcare systems are being reformed across the world. These reforms are redefining the boundaries between medical and paramedical professions, and have resulted in the emergence of advanced practices, notably in nursing. The field of oncology is not immune from these issues. It must deal with the evolution of techniques and technologies, the increase in knowledge as well as an evolution in the needs of patients with cancer. In France, advanced practice nurses appear to have found their place in oncology as they can take an active part in the priorities fixed by the different national cancer plans, notably through the prevention role which is one of the objectives.

[Basis and perspectives of artificial intelligence in radiation therapy]. / Intelligence artificielle et radiothérapie : quelles bases et quelles perspectives ?

Artificial intelligence is a highly polysemic term. In computer science, with the objective of being able to solve totally new problems in new contexts, artificial intelligence includes connectionism (neural networks) for learning and logics for reasoning. Artificial intelligence algorithms mimic tasks normally requiring human intelligence, like deduction, induction, and abduction. All apply to radiation oncology. Combined with radiomics, neural networks have obtained good results in image classification, natural language processing, phenotyping based on electronic health records, and adaptive radiation therapy. General adversial networks have been tested to generate synthetic data. Logics based systems have been developed for providing formal domain ontologies, supporting clinical decision and checking consistency of the systems. Artificial intelligence must integrate both deep learning and logic approaches to perform complex tasks and go beyond the so-called narrow artificial intelligence that is tailored to perform some highly specialized task. Combined together with mechanistic models, artificial intelligence has the potential to provide new tools such as digital twins for precision oncology.

[Issues of oral targeted therapies in daily clinical practice: 5th edition of the congress of pharmacology of anticancer drugs]. / Enjeux et écueils des thérapies ciblées orales en pratique clinique quotidienne : 5e journée de pharmacologie des anti-tumoraux.

Oral targeted therapies are a growing class of medication. After clinical trials conducted on a selected population, these molecules are usually approved at a fixed dose. However, oral tyrosine kinase inhibitors are characterized by a large intra and inter-individual pharmacokinetic variability, and a narrow therapeutic index. Hence, their prescription is hazardous and unsafe in non-selected people from daily clinical practice. The increasing number of available targeted therapies point out new challenges. These challenges should especially concern prescription for out of the ordinary patients, rules for dose adjustment according to factors of frailty. The ultimate goal is to ensure a safe and individualized prescription. Moreover, many of these molecules are metabolized by the CYP3A4, leading to a serious risk of drug interaction. These interactions might involve not only conventional medicine but also alternative and complementary medicines. These latter are more and more common but oncologists often lack experience about them. Finally, the oral route raises the issues of adherence, and the question of its assessment should now become a permanent part of patients care.

[Genomic characterization of head and neck squamous cell carcinoma: Impact and challenges for therapeutic management]. / Caractérisation génomique des carcinomes épidermoïdes de la tête et du cou : impact et enjeux pour la prise en charge thérapeutique.

Squamous cell carcinomas are the most frequent subgroup among head and neck malignant tumors (HNSCC). Tobacco (±alcohol) and HPV infection, the two main risk factors, define two entities with distinct anatomo-clinical and genetic features. HPV-positive, non-tobacco-related HNSCCs are associated with a better prognosis, a rather simple genomic profile, frequent activating mutations of genes involved in pi3kinase-pathway, and the scarcity of mutations of tumor suppressor genes. HPV-negative, tobacco-related HNSCC are genetically more complex, are characterized by almost mandatory inactivating mutations/deletions of tumor suppressor genes (TP53, CDKN2A) and the possible, but less frequent, activating mutations or amplifications of some oncogenes that encode for cell cycle proteins or receptors with tyrosine kinase activity. This review describes the genetic features of HNSCC and discusses how these abnormalities could be incorporated into a therapeutic approach.

[Precision medicine: A major step forward in specific situations, a myth in refractory cancers?] / Médecine de précision : une avancée majeure dans des situations spécifiques, un mythe dans les cancers réfractaires ?

In recent years, high-throughput sequencing techniques have been developed for cancerology and many clinical trials are currently structured around biomarkers that can guide specific treatment choices. This approach is characteristic of precision medicine, which is actually a concept initiated several decades ago with, for example, retinoic acid in promyelocytic leukemia. This paper will review the different types of molecular alterations and « -omics ¼ biological analyses, bioinformatics tools, coupled drug/biomarkers already validated, the ethical issues of whole genomic sequencing of an individual as part of an inclusion in a clinical trial and finally the first results of precision medicine trials. The AcSé crizotinib program, supported by the Inca (french Cancer National Institute), is emblematic of a success of this personalized medicine illustrated by 4 points: the discovery of a cohort of patients with lung cancer with a ROS1 rearrangement characteristic of a sensitivity to crizotinib, a rapid availability of this innovation through the implementation of a temporary recommendation for use (ANSM), the obtention of a conditional marketing authorization by the pharmaceutical industry and finally, financial assumption of responsibility by French social security (HAS), despite preliminary and non-comparative data. In the case of cancers refractory to standard chemotherapy, and regarding our system of access to drugs illustrated by the PROFILER clinical trial, this approach allows the access to a therapeutic drug targeting specific biomarkers only in 7% of patients included. This does not bode well for efficient treatment and even less for survival. Allowing patients to be included in trials that identify molecular targets by molecular screening, and not being able to propose the drug of interest is a traumatic event for those patients who live in the hope of an immediate future. In refractory disease we must rethink precision medicine in a more humanistic vision for our patients and not only in a dimension of medico-industrial promotion. The implementation of a new multi-drug/multi-molecular target program could address this issue.

[Molecular heterogeneity of malignant pleural mesotheliomas]. / Hétérogénéité moléculaire des mésothéliomes pleuraux malins.

Malignant pleural mesothelioma (MPM) is predominantly an occupational cancer, most often linked to asbestos exposure. Malignant pleural mesothelioma prognosis is poor with a short survival median, due to the aggressiveness of tumor cells and the weak efficiency of conventional anti-cancer therapies. Clinical, histological, and molecular data suggest tumor heterogeneity between patients as it was also shown for other cancer types. Consequently, there is an urgent need to develop new therapies that take into account this heterogeneity and the molecular characteristics of malignant pleural mesothelioma, in particular by identifying new anti-cancer drugs targeting the molecular specificities of each malignant pleural mesothelioma. Malignant pleural mesothelioma is characterized by numerous molecular alterations at the chromosomal, genetic and epigenetic levels. Molecular classification based on gene expression profile has firstly defined two tumor groups, C1 and C2, and more recently, four groups. By integrating genetic and transcriptomic analysis, a C2LN tumor subgroup of the C2 group has been identified and characterized. In addition to tumor heterogeneity between patients, intra-tumor heterogeneity is supported by several evidences. Most therapeutic strategies that take into account the tumor molecular characteristics have focused on targeted therapies based on mutated genes. A more appropriate strategy would be to consider better-defined tumor groups on the basis of several molecular alterations types as it has been proposed for the C2LN subgroup. A robust definition of homogeneous tumor groups sharing common molecular characteristics is necessary for the development of effective precision medicine for malignant pleural mesothelioma.